The detection of tissue margins to surgically remove tumors is highly subjective. Medical personnel would greatly benefit from methods for the quantitative identification of margins, between neoplastic and non-neoplastic tissue. Such methods hold potential for ensuring sufficient tissue near the tumor is surgically removed thereby preventing the reoccurrence of the tumor. Brain tissue is a particularly important case where ill-defined margins may disrupt important functions of the brain.
Current operative microscope methods are inadequate for the intra-operative differentiation of primary central nervous system neoplastic tissue from non-neoplastic tissue. Digital image guidance techniques are hampered by the problems of structural shift which occurs during resection such that the pre-operative images do not correlate to the operative field. Ultrasonographic methods have limitations associated with tissue swelling or the presence of hemorrhage. Neurosurgical investigators have used various injectable dyes or stains to demark tumor margins to resolve the problems associated with the current methods. These injectable agents demarcate boundaries which are limited to a physical process, the breakdown of the blood-brain barrier, rather than identifying specific neoplastic boundaries.
Various researchers have applied Raman spectroscopy to characterize a wide variety of biological tissue as described in: Hanlon et. al., 2000, Physics in Medicine and Biology, 45: R1-R59; Lakshmi et al., 2002, Radiation Research, 157(2), 175-182; Mizuno et al., 1992, Neuroscience Letters, 141 (1), 47-52; Mizuno et al., 1994, Journal of Raman Spectroscopy, 25, 25-29; Sajid et al., 1997, Journal of Raman Spectroscopy, 28, 165-169; Dong et al., 2003, Biochemistry, 42, 2768-2773; and Mirura et al., Journal of Raman Spectroscopy, 2002, 33, 530-535, each of which is incorporated by reference in its entirety.
In the case of brain tissue, Raman spectroscopy has been performed on the cerebral cortex, white matter of the cerebrum and the thalamus, using near infrared illumination. The intensity ratios of the amide I bands compared to bands representative of CH bonds were used to differentiate between grey and white matters. These intensity ratios were also used to distinguish between normal brain tissue and brain tumor. Other studies have shown changes in the Raman spectra of biological and brain tissue of mice after the mice were subjected to irradiation. Raman spectroscopy has also been used to monitor amyloid β-plaques deposited in the brains with Alzheimer's disease (“AD”). Using NIR illumination, clear differences between the Raman spectra of AD tissue and non-diseased tissue were observed. Features of the Raman spectrum appear indicative of β-pleated sheet conformation were observed for amyloid β-protein in senile plaques. The lipid-to-protein intensity ratios were used to monitor disease-related changes in the tissue composition.
Chemical imaging combines spectroscopy and digital imaging processing to provide image with contrast based on chemical structure that detail morphology, composition and structure. Raman spectroscopy and Raman chemical imaging are non-destructive, non-contact, and require little to no sample preparation. Raman approaches for the evaluation of biological systems including cells and tissue samples have the distinct advantage over reagent-based methodologies because Raman signals can be measured from the molecular constituents of a sample directly. Raman assessment of cell and tissue samples can be applied to a broad group of cells and tissues and even hold potential for assessing the presence and effects of pharmaceutical agents in cellular and tissue systems. The use of Raman techniques also holds potential for demonstrating sensitivity to histological distinctions in tissues.
Historically, instruments have used Continuous Wave (CW) laser sources to excite the sample being analyzed. One challenge in deploying a Raman imaging or spectroscopic system in the setting of living tissues is the amount of fluorescence present in tissues due to endogenous fluorophores. This fluorescence may interfere with the Raman signal the sample is exhibiting. Autofluorescence of the sample often plagues CW Raman. With repeated laser exposure, fluorescence will often decrease through the process of photobleaching. Photobleaching can be time consuming, ranging anywhere from minutes to hours. A second challenge is the effect of background or ambient light on Raman measurements, which make Raman detection in many settings more difficult. There exists a need to rapidly analyze a biological sample while simultaneously reducing the effects of autofluorescence. Specifically, such systems and methods are needed to advance analysis of biological tissue samples, specifically to enable differentiation between tissue margins.